Chris Sachs
Co-Founder and CTO of Swim.inc
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Chris Sachs is the co-founder and CTO of Swim.inc, and the the creator and lead developer of Swim, the first open source, full stack streaming application platform. Chris excels at building vertically integrated software stacks from first principles. Leveraging the power of design thinking, Chris strives to solve deep technical problems for the benefit of humans.
Having built dozens of production streaming applications, some of which process millions of events per second, compute hundreds of millions of KPIs per second, and make critical autonomous control decisions, Chris believes that the full potential of streaming data will only be realized when humans are able to oversee—and understand—what their autonomous systems are doing right now, at scale.
Streaming Data to Humans
What role do human operators play in an increasingly automated world? How can mere mortals oversee—and make sense of—what large scale real-time systems are doing right now? Is it possible to get an intuitive high level sense of the real-time state of a hundred million things? Is it useful to do so? Can users explore real-time data with the same specificity they’re accustomed to having for events past?
In this lightning talk, we’ll demonstrate real-world attempts at answering the above questions using full stack, end-to-end streaming data applications, fed by Kafka. We’ll show live demos with interactive, truly real-time browser-based visualization of millions of real-world entities, and their actionable rollups.
Full stack streaming applications that push data all the way through to end-users, with comprehensive application logic in the real-time loop, take Kafka topics to the next level. We’ll share lessons learned—and mistakes to avoid—gleaned from a decade’s experience striving to make cross-domain streaming data accessible to people, so that they may feel more comfortable with—and better in charge of—the global automations they most wish to pursue.
Stateful Microservices for Computing on Streams in Context
Streaming data applications often need to access significant amounts of context in order to make sense of any given Kafka event. Whether such context comes from databases, third party APIs, other high rate streams, or all of the above, stateful microservices provide an efficient way to execute arbitrary application logic with memory latency access to large amounts of relevant context, at the throughput of the fastest data source.
In this talk, we’ll discuss entity-parallel stateful microservices architectures, how they’re partitioned, how they scale, and how to make them robust against failure. We’ll examine a real-world case study of combining half a dozen million event per second firehoses in real-time to autonomously monitor and analyze a nationwide network.
With a concrete use case in hand, we’ll explore patterns for implementing massively parallel time-coherent state machines that mirror the real world. We’ll look at how to continuously compute real-time aggregations and reductions. And we’ll demonstrate how to live rank high level real-time entities by biggest impact, with the intent of focussing users’—and other automated systems’—attention on what matters most right now.
HTTP/2 Streaming APIs for Full Stack Real-Time Applications
Despite being a multiplexed streaming protocol, HTTP/2 is still primarily used to make one-shot remote procedure calls to stateless web services. In this session, we'll explore how to upgrade REST APIs to provide granular streams of real-time state changes, driven by Kafka events.
Creating streaming APIs from Kafka topics enables web browsers, and other API clients, to observe real-time changes to individual entities, without having to consume whole topics. HTTP/2 multiplexing enables applications to dynamically subscribe to the real-time state of many entities at once, over a single connection.
After we cover the basics, we’ll compare and contrast streaming APIs with REST APIs, using a simple command line client to illustrate. Then we’ll dive deeper into design patterns and best practices for incorporating streaming APIs into large scale, data intensive streaming applications.
We’ll demonstrate real-time maps that dynamically stream the live state of thousands of real-world entities, while only streaming what’s actually visible on screen at any given time. And we’ll close with a whirlwind tour of UX design patterns that showcase how streaming APIs can create live windows into our worlds—both real and virtual.
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